Performance Characterization of EDA and Its Potential to Improve Decision Making in Product Batch Release - Good Cascade Impactor Practices, AIM and EDA for Orally Inhaled Products - page 172

Table 8.2 List of products used for MSA and OCC comparisons in this chapter
File code Product type Number of CI determinations ( n )

w9k201 HFA suspension MDI 80

w9j901 HFA suspension MDI 39

w9j801 HFA solution MDI 201

w9jk01 Dry powder inhaler 279

w9k901 Dry powder inhaler 279

w9j601 CFC suspension MDI 43

w9k001 CFC suspension MDI 272

w9kw01 CFC suspension MDI 272

product in question was a dry powder formulation of the API in association with
lactose carrier particles. The product has been used in dose-ranging clinical
studies at four nominal strengths, which were all designed to be dose propor-
tional in terms of respirable dose (≡ FPM ). The test method applied to control
the product was NGI (with pre-separator) at a flow rate of 60 L/min with data
collected on a single actuation of the device. Final quantitative analysis of each
of the NGI stages was conducted by reverse phase HPLC using an external ref-
erence standard. For the purposes of the PCA work, the data for each of the
stages of the NGI was normalized as a percentage of the nominal dose to allow
all four product strengths to be evaluated together. The data from the mouth-
piece, induction port (MT), and pre-separator (PS) were excluded from the PCA
model for the following reasons:

1. Only the profile of the respirable dose was of interest since this would be indica-
tive of lung distribution.
2. Inclusion of MT and PS data did not change the model but did increase the noise
level.

SIMCA (Umetrics AB, Umeå, Sweden) was used to conduct the principal com-
ponent analysis with the data being mean-centered throughout.

8.4 Approaches to the Evaluation of CI-Derived Metric

Performance in Assessment of OIP Quality

8.4.1 Introduction to MSA, OCC, and PCA Approaches

There are many facets of a test or measurement that are usually evaluated prior to
adoption for a specific measurement situation. These include ease of use, cost of
instrumentation, acceptance (by regulators and wider industry), and finally the
quality of results obtained. It is this last item, the quality of results, which is the
focus of this chapter. Specifically, the intent is to characterize the performance of
the EDA metrics from the perspective of their use within a quality control strategy.